scholarly journals Alteration of the corpus callosum in patients with Alzheimer’s disease: Deep learning-based assessment

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0259051
Author(s):  
Sadia Kamal ◽  
Ingyu Park ◽  
Yeo Jin Kim ◽  
Yun Joong Kim ◽  
Unjoo Lee
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Cognitive Function ◽  
Corpus Callosum ◽  
The Body ◽  
Learning Technology ◽  
Midsagittal Plane ◽  
Clinical Dementia Rating ◽  
Mri Measurements

Background Several studies have reported changes in the corpus callosum (CC) in Alzheimer’s disease. However, the involved region differed according to the study population and study group. Using deep learning technology, we ensured accurate analysis of the CC in Alzheimer’s disease. Methods We used the Open Access Series of Imaging Studies (OASIS) dataset to investigate changes in the CC. The individuals were divided into three groups using the Clinical Dementia Rating (CDR); 94 normal controls (NC) were not demented (NC group, CDR = 0), 56 individuals had very mild dementia (VMD group, CDR = 0.5), and 17 individuals were defined as having mild and moderate dementia (MD group, CDR = 1 or 2). Deep learning technology using a convolutional neural network organized in a U-net architecture was used to segment the CC in the midsagittal plane. Total CC length and regional magnetic resonance imaging (MRI) measurements of the CC were made. Results The total CC length was negatively associated with cognitive function. (beta = -0.139, p = 0.022) Among MRI measurements of the CC, the height of the anterior third (beta = 0.038, p <0.0001) and width of the body (beta = 0.077, p = 0.001) and the height (beta = 0.065, p = 0.001) and area of the splenium (beta = 0.059, p = 0.027) were associated with cognitive function. To distinguish MD from NC and VMD, the receiver operating characteristic analyses of these MRI measurements showed areas under the curves of 0.65–0.74. (total CC length = 0.705, height of the anterior third = 0.735, width of the body = 0.714, height of the splenium = 0.703, area of the splenium = 0.649). Conclusions Among MRI measurements, total CC length, the height of the anterior third and width of the body, and the height and area of the splenium were associated with cognitive decline. They had fair diagnostic validity in distinguishing MD from NC and VMD.

Computer aided Alzheimer's disease diagnosis by an unsupervised deep learning technology

2020 ◽  
Vol 392 ◽  
pp. 296-304 ◽  
Author(s):  
Xiuli Bi ◽  
Shutong Li ◽  
Bin Xiao ◽  
Yu Li ◽  
Guoyin Wang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Disease Diagnosis ◽  
Learning Technology ◽  
Computer Aided ◽  
Unsupervised Deep Learning ◽  
Alzheimer’S Disease Diagnosis

scholarly journals Polypharmacy in Alzheimer's disease patients in Brazil: Guidance for pharmaceutical assistance

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 2068 ◽  
Author(s):  
Felipe Nathanael Coelho Vaz ◽  
Luana Bortoluzzi Trombim ◽  
Guilherme Barroso L. de Freitas ◽  
Maria Vaitsa Loch Haskel ◽  
Giovana dos Santos ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Interactions ◽  
Mini Mental State Examination ◽  
Study Data ◽  
Demographic Variables ◽  
The Body ◽  
Disease Stage ◽  
Clinical Dementia Rating ◽  
State Examination

Background: Elderly patients frequently have concomitant diseases, triggering the necessity of utilizing several different medications, which can cause adverse events associated with therapy, called polypharmacy. This study aimed to evaluate the main concomitant diseases with Alzheimer's disease (AD) and discuss possible interactions between drugs utilized to treat dementia and its comorbidities, and indicate safe medicines for patients with AD. Methods: 41 individuals with AD who withdraw medicines for dementia from the Brazilian public health system (SUS) participated in this study. Data collection was performed using three questionnaires: 1) Clinical Dementia Rating, to verify disease stage; 2) Mini–mental state examination, to measure cognitive impairment; and 3) Sociodemographic analysis, to evaluate concomitant diseases, utilized drugs, drug-drug interactions, among other demographic variables. Statistical analyses were performed using SPSS and data was presented as relative frequency. Results: The results of this study showed that the most frequent concomitant diseases with AD are: systemic arterial hypertension, depression, diabetes mellitus, and hypercholesterolemia. Polypharmacy was observed in 95.12% of patients. The pharmacologic classes that presented interactions with AD medications were anxiolytics, antidepressants, antipsychotics, antihypertensives, and antidiabetics. Conclusion: In the present study, polypharmacy in patients with AD and other concomitant diseases has been characterized. The average number of drugs that these patients ingested was seven per day, and this leads to drug interactions, which are potentially damaging to the body. Consequently, we have tried to reduce these interactions, by suggesting drugs that are safer, for example furosemide instead of amlodipine to treat hypertension.

A Cross-Sectional Study on the Impact of Arterial Stiffness on the Corpus Callosum, a Key White Matter Tract Implicated in Alzheimer’s Disease

2020 ◽  
Vol 77 (2) ◽  
pp. 591-605
Author(s):  
Atef Badji ◽  
Adrián Noriega de la Colina ◽  
Tommy Boshkovski ◽  
Dalia Sabra ◽  
Agah Karakuzu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Older Adults ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Arterial Stiffness ◽  
Corpus Callosum ◽  
Water Diffusion ◽  
The Body ◽  
Extracellular Water ◽  
The Impact

Background: Vascular risk factors such as arterial stiffness play an important role in the etiology of Alzheimer’s disease (AD), presumably due to the emergence of white matter lesions. However, the impact of arterial stiffness to white matter structure involved in the etiology of AD, including the corpus callosum remains poorly understood. Objective: The aims of the study are to better understand the relationship between arterial stiffness, white matter microstructure, and perfusion of the corpus callosum in older adults. Methods: Arterial stiffness was estimated using the gold standard measure of carotid-femoral pulse wave velocity (cfPWV). Cognitive performance was evaluated with the Trail Making Test part B-A. Neurite orientation dispersion and density imaging was used to obtain microstructural information such as neurite density and extracellular water diffusion. The cerebral blood flow was estimated using arterial spin labelling. Results: cfPWV better predicts the microstructural integrity of the corpus callosum when compared with other index of vascular aging (the augmentation index, the systolic blood pressure, and the pulse pressure). In particular, significant associations were found between the cfPWV, an alteration of the extracellular water diffusion, and a neuronal density increase in the body of the corpus callosum which was also correlated with the performance in cognitive flexibility. Conclusion: Our results suggest that arterial stiffness is associated with an alteration of brain integrity which impacts cognitive function in older adults.

scholarly journals The Association of Blood-Based Inflammatory Factors IL-1β, TGF-β and CRP with Cognitive Function in Alzheimer’s Disease and Mild Cognitive Impairment

2021 ◽  
Vol 18 (1) ◽  
pp. 11-18
Author(s):  
Jun Kyung Park ◽  
Kang Joon Lee ◽  
Ji Yeon Kim ◽  
Hyun Kim
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Function ◽  
Depression Scale ◽  
Disease Stage ◽  
Inflammatory Factors ◽  
Clinical Dementia Rating ◽  
Global Deterioration Scale

Objective Many patients suffer from dementia in its most common form, Alzheimer’s disease (AD). In this study, the levels of IL-1β, TGF-β and CRP, which are involved in the inflammatory response in Alzheimer’s disease and its mild cognitive impairment (MCI), were measured and analyzed.Methods Seventy nine subjects participated in this study (mean age: 75.56 years, female: 54.3%, AD: 26, MCI: 28, normal: 25). The overall cognitive function of the subjects and the severity of the disease stage were assessed using the Mini-Mental State Examination (MMSE-K), the Clinical Dementia Rating (CDR), the Global Deterioration Scale (GDS) and the Geriatric Depression Scale-Korean (GDS-K).Results It was observed that patients with AD had significantly higher levels of IL-1β and TGF-β than the patients with MCI and normal controls. In addition, the MCI group showed a statistically significantly higher TGF-β concentration than the normal group.Conclusion These results suggest that IL-1β and TGF-β may be useful biological markers for patients with Alzheimer’s disease.

Deformation-based Statistical Shape Analysis of the Corpus Callosum in Mild Cognitive Impairment and Alzheimer’s Disease

2018 ◽  
Vol 15 (12) ◽  
pp. 1151-1160 ◽  
Author(s):  
Zihan Jiang ◽  
Huilin Yang ◽  
Xiaoying Tang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Corpus Callosum ◽  
Shape Analysis ◽  
Disease Assessment ◽  
Statistical Shape Analysis ◽  
Superior Part ◽  
Statistical Shape

Objective: In this study, we investigated the influence that the pathology of Alzheimer’s disease (AD) exerts upon the corpus callosum (CC) using a total of 325 mild cognitive impairment (MCI) subjects, 155 AD subjects, and 185 healthy control (HC) subjects. Method: Regionally-specific morphological CC abnormalities, as induced by AD, were quantified using a large deformation diffeomorphic metric curve mapping based statistical shape analysis pipeline. We also quantified the association between the CC shape phenotype and two cognitive measures; the Mini Mental State Examination (MMSE) and the Alzheimer’s Disease Assessment Scale-Cognitive Behavior Section (ADAS-cog). To identify AD-relevant areas, CC was sub-divided into three subregions; the genu, body, and splenium (gCC, bCC, and sCC). Results: We observed significant shape compressions in AD relative to that in HC, mainly concentrated on the superior part of CC, across all three sub-regions. The HC-vs-MCI shape abnormalities were also concentrated on the superior part, but mainly occurred on bCC and sCC. The significant MCI-vs-AD shape differences, however, were only detected in part of sCC. In the shape-cognition association, significant negative correlations to ADAS-cog were detected for shape deformations at regions belonging to gCC and sCC and significant positive correlations to MMSE at regions mainly belonging to sCC. Conclusion: Our results suggest that the callosal shape deformation patterns, especially those of sCC, linked tightly to the cognitive decline in AD, and are potentially a powerful biomarker for monitoring the progression of AD.

Development and Evaluation of a Tactile Cognitive Function Test Device for Alzheimer’s Disease Early Detection

2015 ◽  
Vol 3 (2) ◽  
pp. 58-65 ◽  
Author(s):  
Jiajia Yang ◽  
Mohd Usairy Syafiq ◽  
Yinghua Yu ◽  
Satoshi Takahashi ◽  
Zhenxin Zhang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Function ◽  
Early Detection ◽  
Test Device ◽  
Function Test ◽  
Cognitive Function Test

scholarly journals Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Manan Binth Taj Noor ◽  
Nusrat Zerin Zenia ◽  
M Shamim Kaiser ◽  
Shamim Al Mamun ◽  
Mufti Mahmud
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Neurological Disorders ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Comparative Performance

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

scholarly journals Topographic patterns of white matter hyperintensities are associated with multimodal neuroimaging biomarkers of Alzheimer’s disease

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Malo Gaubert ◽  
Catharina Lange ◽  
Antoine Garnier-Crussard ◽  
Theresa Köbe ◽  
Salma Bougacha ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Glucose Metabolism ◽  
Corpus Callosum ◽  
Gray Matter ◽  
Fdg Pet ◽  
White Matter Hyperintensities ◽  
Gray Matter Volume ◽  
Matter Volume

Abstract Background White matter hyperintensities (WMH) are frequently found in Alzheimer’s disease (AD). Commonly considered as a marker of cerebrovascular disease, regional WMH may be related to pathological hallmarks of AD, including beta-amyloid (Aβ) plaques and neurodegeneration. The aim of this study was to examine the regional distribution of WMH associated with Aβ burden, glucose hypometabolism, and gray matter volume reduction. Methods In a total of 155 participants (IMAP+ cohort) across the cognitive continuum from normal cognition to AD dementia, FLAIR MRI, AV45-PET, FDG-PET, and T1 MRI were acquired. WMH were automatically segmented from FLAIR images. Mean levels of neocortical Aβ deposition (AV45-PET), temporo-parietal glucose metabolism (FDG-PET), and medial-temporal gray matter volume (GMV) were extracted from processed images using established AD meta-signature templates. Associations between AD brain biomarkers and WMH, as assessed in region-of-interest and voxel-wise, were examined, adjusting for age, sex, education, and systolic blood pressure. Results There were no significant associations between global Aβ burden and region-specific WMH. Voxel-wise WMH in the splenium of the corpus callosum correlated with greater Aβ deposition at a more liberal threshold. Region- and voxel-based WMH in the posterior corpus callosum, along with parietal, occipital, and frontal areas, were associated with lower temporo-parietal glucose metabolism. Similarly, lower medial-temporal GMV correlated with WMH in the posterior corpus callosum in addition to parietal, occipital, and fontal areas. Conclusions This study demonstrates that local white matter damage is correlated with multimodal brain biomarkers of AD. Our results highlight modality-specific topographic patterns of WMH, which converged in the posterior white matter. Overall, these cross-sectional findings corroborate associations of regional WMH with AD-typical Aß deposition and neurodegeneration.

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